Riemann-Stieltjes operators and multipliers on QpQ_p spaces in the unit ball of CnC^n

This paper is devoted to characterizing the Riemann-Stieltjes operators and pointwise multipliers acting on M${\rm \ddot{o}}$bius invariant spaces $Q_p$, which unify BMOA and Bloch space in the scale of $p$. The boundedness and compactness of these operators on $Q_p$ spaces are determined by means of an embedding theorem, i.e. $Q_p$ spaces boundedly embedded in the non-isotropic tent type spaces $T_q^\infty$.Comment: 16 page

Dynamic responses of non-ballast track structures subjected to high-speed train loads at the welded rail joint

This paper presents a numerical study on the dynamic responses of the non-ballast track structures under the high-speed train load that is excited by the rail irregularity at the welded rail joint. In this study, a multi-body dynamics model with 10 degrees of freedom is built to model a high-speed vehicle and a finite element model is established to simulate the non-ballast track. A mathematical model is also given to characterize the geometry of the local rail irregularity at the welded rail joint. By coupling the high-speed vehicle model and the track model and taking the mathematical model of the welded rail joint as an input, the dynamic responses of the non-ballast track structures under a rail vehicle running at a high speed are simulated and discussed in this paper. The wheel/rail force and the rail and slab vibration acceleration are investigated to demonstrate the significant dynamic effects on the track structures due to the welded rail joint

Ordinal sums of triangular norms on a bounded lattice

The ordinal sum construction provides a very effective way to generate a new triangular norm on the real unit interval from existing ones. One of the most prominent theorems concerning the ordinal sum of triangular norms on the real unit interval states that a triangular norm is continuous if and only if it is uniquely representable as an ordinal sum of continuous Archimedean triangular norms. However, the ordinal sum of triangular norms on subintervals of a bounded lattice is not always a triangular norm (even if only one summand is involved), if one just extends the ordinal sum construction to a bounded lattice in a na\"{\i}ve way. In the present paper, appropriately dealing with those elements that are incomparable with the endpoints of the given subintervals, we propose an alternative definition of ordinal sum of countably many (finite or countably infinite) triangular norms on subintervals of a complete lattice, where the endpoints of the subintervals constitute a chain. The completeness requirement for the lattice is not needed when considering finitely many triangular norms. The newly proposed ordinal sum is shown to be always a triangular norm. Several illustrative examples are given

Simulated identification on dynamic characteristics of large heavy-load bearing

It’s difficult to test repeatedly for large heavy-load bearings (LHLBs) with full-scale and real load due to complexity and costliness, so simulated identification on dynamic characteristics of 1750 MW nuclear generator bearing with diameter 800 mm and specific pressure 3.3 MPa is provided in this paper. The identification model of bearing dynamic characteristic is established, the calculating method of positive and negative dynamic problems is provided, and effects of signal disturbances on identification precision are analyzed. The results show that the LHLBs’ permitted displacement disturbance should not be over 5 μm and the permitted ratio of dynamic load and static load is about 1 %-2 %, which is different from common knowledge of 15 %-20 % for small light-load bearings. If identification error of the main stiffness and main damping coefficients is less than 5 %, the amplitude of periodical disturbance of the dynamic load and displacement signals should be less than 5 %. If identification error of the main damping coefficients is less than 10 %, the phase of these two signals should be less than 1°. The roundness error and rotation error of the large shaft should be eliminated

Application of numerical simulation on eliminating shrinkage defect of automobile wheel hub

Shrinkage defect is a serious problem encountered during the development of automobile wheel hub made from ductile cast iron. In order to find out the reason for shrinkage formation and eliminate it in time, numerical simulation technology was performed to analyze the casting solidification process, and two modified casting projects were brought forward. Based on the simulation result, the solidification characteristics of the original project were compared with two modified projects and accordingly the optimized casting project with chill and rider feeding was selected for application during the development of the wheel hub. The result shows that casting shrinkage defect has been effectively controlled and the trial production cycle of the wheel hub was significantly shortened

Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing

Residual networks have shown great success and become indispensable in today's deep models. In this work, we aim to re-investigate the training process of residual networks from a novel social psychology perspective of loafing, and further propose a new training strategy to strengthen the performance of residual networks. As residual networks can be viewed as ensembles of relatively shallow networks (i.e., \textit{unraveled view}) in prior works, we also start from such view and consider that the final performance of a residual network is co-determined by a group of sub-networks. Inspired by the social loafing problem of social psychology, we find that residual networks invariably suffer from similar problem, where sub-networks in a residual network are prone to exert less effort when working as part of the group compared to working alone. We define this previously overlooked problem as \textit{network loafing}. As social loafing will ultimately cause the low individual productivity and the reduced overall performance, network loafing will also hinder the performance of a given residual network and its sub-networks. Referring to the solutions of social psychology, we propose \textit{stimulative training}, which randomly samples a residual sub-network and calculates the KL-divergence loss between the sampled sub-network and the given residual network, to act as extra supervision for sub-networks and make the overall goal consistent. Comprehensive empirical results and theoretical analyses verify that stimulative training can well handle the loafing problem, and improve the performance of a residual network by improving the performance of its sub-networks. The code is available at https://github.com/Sunshine-Ye/NIPS22-ST .Comment: NIPS2022 accep